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Mathematical Models of Personalized Medicine
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Mathematical Models of Personalized Medicine

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Omics/Informatics".

Deadline for manuscript submissions: 25 October 2024 | Viewed by 2279

Special Issue Editors

Prof. Dr. Paolo Castorina

E-Mail Website
Guest Editor
1. Istituto Nazionale Fisica Nucleare, Catania, Italy
2. Institute of Particle and Nuclear Physics, Charles University, Prague, Czech Republic
Interests: theoretical physics; growth laws in physics and other systems; models of cancer evolution and therapy effects; mathematical oncology
Dr. Alberto D'Onofrio

E-Mail Website
Guest Editor
Department of Mathematics and Geosciences, University of Trieste, Via Alfonso Valerio 12/1 Building H2bis, 34127 Trieste, Italy
Interests: theoretical biophysics; applied computer sciences; mathematical oncology; applied statistical physics; mathematical epidemiology of infectious diseases

Special Issue Information

Dear Colleagues,

Background: Mathematical and computational models are an important tool to obtain useful information on tumor growth, response to therapy and survival probability. A further step towards clinical applications is the development of a flexible mathematical/algorithmic formulation for personalized medicine.

Aim and scope: This Special Issue will focus on mathematical and computational modeling for cancer growth, evolution and therapy, including chemotherapy, radiotherapy, immunotherapy and their synergies.

History: In recent decades, the quantitative formulation of cancer progression has made great developments using microscopical and macroscopical models.

Cutting-edge research: Recently, the mathematical/computational study of synergy among different therapies has attracted increased interest. The patient-oriented forecast for clinical decisions is another important sector in its evolution.

We are looking for original studies based on mathematical and computational models and/or validated phenomenological approaches for patient-oriented tumor progression during and after therapy.

Prof. Dr. Paolo Castorina
Dr. Alberto D'Onofrio
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • tumor growth and therapy
  • patient-oriented mathematical models
  • complex systems for personalized oncology
  • systems biology for personalized oncology
  • nonequilibrium statistical physics for personalized oncology

Published Papers (2 papers)

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Research

37 pages, 6817 KiB  
Article
A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin
by Eleni Kolokotroni, Daniel Abler, Alokendra Ghosh, Eleftheria Tzamali, James Grogan, Eleni Georgiadi, Philippe Büchler, Ravi Radhakrishnan, Helen Byrne, Vangelis Sakkalis, Katerina Nikiforaki, Ioannis Karatzanis, Nigel J. B. McFarlane, Djibril Kaba, Feng Dong, Rainer M. Bohle, Eckart Meese, Norbert Graf and Georgios Stamatakos
J. Pers. Med. 2024, 14(5), 475; https://doi.org/10.3390/jpm14050475 - 29 Apr 2024
Viewed by 662
Abstract
The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine [...] Read more.
The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary. Full article
(This article belongs to the Special Issue Mathematical Models of Personalized Medicine)
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11 pages, 285 KiB  
Article
Forecasting Individual Patients’ Best Time for Surgery in Colon-Rectal Cancer by Tumor Regression during and after Neoadjuvant Radiochemotherapy
by Emanuele Martorana, Paolo Castorina, Gianluca Ferini, Stefano Forte and Marzia Mare
J. Pers. Med. 2023, 13(5), 851; https://doi.org/10.3390/jpm13050851 - 18 May 2023
Cited by 1 | Viewed by 1128
Abstract
The standard treatment of locally advanced rectal cancer is neoadjuvant chemoradiotherapy before surgery. For those patients experiencing a complete clinical response after the treatment, a watch-and-wait strategy with close monitoring may be practicable. In this respect, the identification of biomarkers of the response [...] Read more.
The standard treatment of locally advanced rectal cancer is neoadjuvant chemoradiotherapy before surgery. For those patients experiencing a complete clinical response after the treatment, a watch-and-wait strategy with close monitoring may be practicable. In this respect, the identification of biomarkers of the response to therapy is extremely important. Many mathematical models have been developed or used to describe tumor growth, such as Gompertz’s Law and the Logistic Law. Here we show that the parameters of those macroscopic growth laws, obtained by fitting the tumor evolution during and immediately after therapy, are a useful tool for evaluating the best time for surgery in this type of cancer. A limited number of experimental observations of the tumor volume regression, during and after the neoadjuvant doses, permits a reliable evaluation of a specific patient response (partial or complete recovery) for a later time, and one can evaluate a modification of the scheduled treatment, following a watch-and-wait approach or an early or late surgery. Neoadjuvant chemoradiotherapy effects can be quantitatively described by applying Gompertz’s Law and the Logistic Law to estimate tumor growth by monitoring patients at regular intervals. We show a quantitative difference in macroscopic parameters between partial and complete response patients, reliable for estimating the treatment effects and best time for surgery. Full article
(This article belongs to the Special Issue Mathematical Models of Personalized Medicine)
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